class_docstr: DropFeatures() drops a list of variables indicated by the user from the dataframe.
  
  More details in the :ref:`User Guide <drop_features>`.
  
  Parameters
  ----------
  features_to_drop: str or list
      Variable(s) to be dropped from the dataframe
  
  Attributes
  ----------
  features_to_drop_:
      The features that will be dropped.
  
  feature_names_in_:
      List with the names of features seen during `fit`.
  
  n_features_in_:
      The number of features in the train set used in fit.
  
  Methods
  -------
  fit:
      This transformer does not learn any parameter.
  
  fit_transform:
      Fit to data, then transform it.
  
  get_feature_names_out:
      Get output feature names for transformation.
  
  get_support:
      Get a mask, or integer index, of the features selected.
  
  get_params:
      Get parameters for this estimator.
  
  set_params:
      Set the parameters of this estimator.
  
  transform:
      Drops indicated features.
code: def __init__(self, features_to_drop: List[Union[str, int]]):
      if (
          not isinstance(features_to_drop, (str, list))
          or len(features_to_drop) == 0
      ):
          raise ValueError(
              f"features_to_drop should be a list with the name of the variables "
              f"you wish to drop from the dataframe. Got {features_to_drop} instead."
          )
  
      self.features_to_drop = features_to_drop
  
file_path: feature_engine/feature_engine/selection/drop_features.py
imports: from typing import List, Union
  import pandas
  from feature_engine.dataframe_checks import check_X
  from feature_engine.selection.base_selector import BaseSelector
  from feature_engine.tags import _return_tags
label_desc: Use image node_id 1 to create a new DropFeatures object from inherited base classes: BaseSelector with example: obj = DropFeatures(features_to_drop)